368. Performance Characteristics of Sequencing Assays for Identification of the SARS-CoV-2 Viral Genome

Abstract Background As the SARS-CoV-2 (SCV-2) virus evolves, diagnostics and vaccines against novel strains rely on viral genome sequencing. Researchers have gravitated towards the cost-effective and highly sensitive amplicon-based (e.g. ARTIC) and hybrid capture sequencing (e.g. SARS-CoV-2 NGS Assay) to selectively target the SCV-2 genome. We provide an in silico model to compare these 2 technologies and present data on the high scalability of the Research Use Only (RUO) workflow of the SARS-CoV-2 NGS Assay. Methods In silico work included alignments of 383,656 high-quality genome sequences belonging to variant of concern (VOC) or variant of interest (VOI) isolates (GISAID). We profiled mismatches and sequencing dropouts using the ARTIC V3 primers, SARS-CoV-2 NGS Assay probes (Twist Bioscience) and 11 synthesized viral sequences containing mutations and compared the performance of these assays using clinical samples. Further, the miniaturized hybrid capture workflow was optimized and evaluated to support high-throughput (384-plex). The sequencing data was processed by COVID-DX software. Results We detected 101,432 viruses (27%) with > = 1 mismatch in the last 6 base pairs of the 3’ end of ARTIC primers; of these, 413 had > = 2 mismatches in one primer. In contrast, only 38 viruses (0.01%) had enough mutations ( > = 10) in a hybrid capture probe to have a similar effect on coverage. We observed that mutations in ARTIC primers led to complete dropout of the amplicon for 4/11 isolates and diminished coverage in additional 4. Twist probes showed uniform coverage throughout with little to no dropouts. Both assays detected a wide range of variants (~99.9% coverage at 5X depth) in clinical samples (CT value < 30) collected in NY (Spring 2020-Spring 2021). The distribution of the number of reads and on target rates were more uniform among specimens within amplicon-based sequencing. However, uneven genome coverage and primer dropouts, some in the spike protein, were observed on VOC/VOI and other isolates highlighting limitations of an amplicon-based approach. Conclusion The RUO workflow of the SARS-CoV-2 NGS Assay is a comprehensive and scalable sequencing tool for variant profiling, yields more consistent coverage and smaller dropout rate compared to ARTIC (0.05% vs. 7.7%). Disclosures Danny Antaki, PhD, Twist Bioscience (Employee, Shareholder) Mara Couto-Rodriguez, MS, Biotia (Employee) Kristin Butcher, MS, Twist Bioscience (Employee, Shareholder) Esteban Toro, PhD, Twist Bioscience (Employee) Bryan Höglund, BS, Twist Bioscience (Employee, Shareholder) Xavier O. Jirau Serrano, B.S., Biotia (Employee) Joseph Barrows, MS, Biotia (Employee) Christopher Mason, PhD, Biotia (Board Member, Advisor or Review Panel member, Shareholder) Niamh B. O’Hara, PhD, Biotia (Board Member, Employee, Shareholder) Dorottya Nagy-Szakal, MD PhD, Biotia Inc (Employee, Shareholder)

*Indicated optimal cut-off value for hsCRP to predict chest CT-confirmed pneumonia.
ROC Curve of hsCRP to Diagnose of COVID-19 Pneumonia This figure shows ROC curve for hsCRP to diagnose of chest CT-confirmed COVID-19 pneumonia. The area under the ROC curve is 0.82. The optimal cut-off value for hsCRP is 2.00 given sensitivity of 81.9% and specificity of 70.3%.
Conclusion. The hsCRP was the conventional biomarker that had an excellent performance in predicting COVID-19 pneumonia lead to early anti-SARS-CoV-2 treatment. This study demonstrated the potential role of hsCRP combined with clinical assessment in negative chest X-rays to replace chest CT in a high burden COVID-19 country during pandemic situations.
Disclosures. Background. As the SARS-CoV-2 (SCV-2) virus evolves, diagnostics and vaccines against novel strains rely on viral genome sequencing. Researchers have gravitated towards the cost-effective and highly sensitive amplicon-based (e.g. ARTIC) and hybrid capture sequencing (e.g. SARS-CoV-2 NGS Assay) to selectively target the SCV-2 genome. We provide an in silico model to compare these 2 technologies and present data on the high scalability of the Research Use Only (RUO) workflow of the SARS-CoV-2 NGS Assay.
Methods. In silico work included alignments of 383,656 high-quality genome sequences belonging to variant of concern (VOC) or variant of interest (VOI) isolates (GISAID). We profiled mismatches and sequencing dropouts using the ARTIC V3 primers, SARS-CoV-2 NGS Assay probes (Twist Bioscience) and 11 synthesized viral sequences containing mutations and compared the performance of these assays using clinical samples. Further, the miniaturized hybrid capture workflow was optimized and evaluated to support high-throughput (384-plex). The sequencing data was processed by COVID-DX software.
Results. We detected 101,432 viruses (27%) with > = 1 mismatch in the last 6 base pairs of the 3' end of ARTIC primers; of these, 413 had > = 2 mismatches in one primer. In contrast, only 38 viruses (0.01%) had enough mutations ( > = 10) in a hybrid capture probe to have a similar effect on coverage. We observed that mutations in ARTIC primers led to complete dropout of the amplicon for 4/11 isolates and diminished coverage in additional 4. Twist probes showed uniform coverage throughout with little to no dropouts. Both assays detected a wide range of variants (~99.9% coverage at 5X depth) in clinical samples (CT value < 30) collected in NY (Spring 2020-Spring 2021). The distribution of the number of reads and on target rates were more uniform among specimens within amplicon-based sequencing. However, uneven genome coverage and primer dropouts, some in the spike protein, were observed on VOC/VOI and other isolates highlighting limitations of an amplicon-based approach. cycle threshold by total new cases in Massachusetts to reflect temporal trend of Ct and cases. In the multivariable linear regression model, Ct increased with days since symptom onset (P< 0.001). Cycle threshold was inversely associated with disease severity in multivariable logistic regression though (OR 1.06, 95%CI 1.01-1.11, p=0.03), even when controlling for time since symptom onset.  Line represents mean Ct over time period included in this study overlaid on total new cases in Massachusetts. Lower Ct were seen in the course as cases were increasing which peaked as cases stabilized.
Conclusion. Cycle threshold increased with time since symptom onset, consistent with prior data showing increasing Ct from time since infection due to decreasing viral replication. This study showed an inverse relationship between cycle threshold and disease severity, which differs from previous studies which demonstrated higher odds of progression to severe disease and mortality with lower Ct. This finding may reflect the disease severity associated with the secondary inflammatory phase of SARS-CoV-2 seen later in the disease course, although there was only moderate correlation between Ct and time since symptom onset. Further research is needed to better understand the role of Ct in predicting clinical severity of SARS-CoV-2 infections.
Disclosures. All Authors: No reported disclosures